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Related Experiment Video

Updated: Jan 10, 2026

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Three-Dimensional Shape Modeling and Analysis of Brain Structures

Published on: November 14, 2019

Weakly-Supervised Shape Multi-Completion of Point Clouds by Structural Decomposition.

Changfeng Ma, Pengxiao Guo, Shuangyu Yang

IEEE Transactions on Visualization and Computer Graphics

|November 24, 2025

Summary

This summary is machine-generated.

This study introduces a novel weakly-supervised method for 3D shape completion using structural decomposition, improving mesh generation from partial point clouds without Signed Distance Functions (SDFs). The approach enhances robustness and accuracy on real-world data.

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Area of Science:

Computer Vision
3D Reconstruction
Geometric Deep Learning

Background:

Partial point clouds present significant challenges for complete 3D mesh generation.
Existing methods struggle with data accessibility, shape preservation, and robustness on real-scan data.
Signed Distance Functions (SDFs) are often required during training, limiting flexibility.

Purpose of the Study:

To develop an innovative weakly-supervised shape completion method for 3D meshes.
To overcome limitations of current methods by leveraging structural information.
To eliminate the need for SDFs during training.

Main Methods:

A weakly-supervised shape completion method using structural decomposition.
Representing objects as abstract structural frameworks and part details.
Utilizing a completion network for image-based part details and a diffusion-based network for multi-result generation.

Main Results:

Achieved state-of-the-art (SOTA) performance in 3D shape completion.
Demonstrated an average improvement of over 38.1% compared to prior methods.
Showcased superior robustness and accuracy on both artificial and real-scan datasets.

Conclusions:

The proposed method effectively generates complete 3D meshes from partial point clouds.
Weakly-supervised learning via structural decomposition offers a robust alternative to SDF-based training.
The approach significantly advances the field of 3D shape completion.